Systematic review with meta‐analysis of over 90 000 patients. Does fast‐track review diagnose colorectal cancer earlier?
Bibliographic record
Abstract
Summary Background National UK data on colorectal cancer (CRC) stage at diagnosis is incomplete. Site‐specific fast‐track (2‐week wait) cancer data are not collected directly by NHS England. Policy making based on these data alone can lead to inaccuracy. Aims To review available data on key outcomes (cancer conversion rate and stage at diagnosis) for the UK's lower gastrointestinal 2‐week wait pathway. Methods A comprehensive literature search was conducted between 2000 and 2017. Primary outcomes were cancer conversion rate and cancer stage at diagnosis. Results were expressed as proportions with 95% CIs. A random effects model was used for meta‐analysis; heterogeneity was assessed by I2. Results Of 95 papers reviewed, 49 were included in analysis with a total study population of 93,655. Cancer conversion rate was 7.7% (95% CI: 6.9‐8.5). The proportion presenting at Dukes A = 11.2% (95% CI 7.4‐15.6), B = 36.7% (95% CI 30.8‐42.8), C = 35.7% (95% CI: 30.8‐40.8) and D = 11.1% (95% CI 7.3‐15.5). No colonic pathology was diagnosed in 54.6% (95% CI: 46.2‐62.8). Conclusions Only 7.7% of patients referred by the 2‐week wait pathway were found to have CRC. No beneficial effect on stage at diagnosis was found compared to non‐2‐week wait referral pathways. Over half of patients had no colonic pathology and detection of adenomas was very low. These results should prompt a reconsideration of the benefits of the 2‐week wait pathway in CRC diagnosis and outcomes, with more focus on strategies to improve patient selection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".